Find what was said, and when, in every recording.

An audio search API enables developers to index and search audio content programmatically. Instead of manually listening to recordings, you can transcribe speech, extract audio features, and build searchable indexes that support text queries over transcripts, speaker-based filtering, audio similarity search, and event detection. Mixpeek provides a complete audio search infrastructure that handles transcription, embedding generation, indexing, and retrieval in a single platform.

Mixpeek uses production-grade ASR (automatic speech recognition) models like Whisper to transcribe audio into text with word-level timestamps and speaker labels. The transcriptions are indexed into Mixpeek Vector Store namespaces alongside audio embeddings. When you search, Mixpeek's retriever pipelines combine keyword matching over transcripts with vector similarity over audio embeddings, returning precise audio segments with timestamps.

Mixpeek supports all common audio formats including MP3, WAV, FLAC, AAC, OGG, M4A, WMA, and AIFF. Audio files are uploaded to Mixpeek buckets and processed by feature extractors running on Ray GPU clusters. The platform handles format conversion, sample rate normalization, and channel mixing automatically during ingestion.

Yes. Mixpeek extracts speaker embeddings during ingestion using speaker diarization models. These embeddings are stored as searchable vectors in your namespace. You can find all segments spoken by a specific person across your entire audio collection, cluster unknown speakers, and filter search results by speaker identity using retriever pipeline stages.

Deepgram and AssemblyAI are transcription services -- they convert speech to text but leave search infrastructure to you. Mixpeek is a complete search platform: it handles transcription, audio embedding generation, vector indexing, and composable retriever pipelines out of the box. Mixpeek also supports multimodal search, so you can search across audio, video, images, and documents in a single query.

Mixpeek supports both real-time and batch audio processing. For real-time use cases, collection triggers automatically process new audio files as they arrive in your bucket. For batch processing, you can submit large volumes of audio files and Mixpeek's Ray clusters scale horizontally to process them in parallel with progress tracking.

Yes. Beyond speech transcription, Mixpeek's audio feature extractors can classify non-speech audio events such as music, alarms, applause, machinery sounds, environmental noise, and more. These events are indexed as metadata on audio segments, enabling you to filter and search by event type in your retriever pipelines.

Mixpeek treats audio and video as complementary modalities within a unified namespace. When you process a video file, Mixpeek extracts both visual features (frames, objects, scenes) and audio features (transcripts, speaker embeddings, audio events) and indexes them together. A single retriever query can search across both the audio track and visual content of your video collection simultaneously.